56,555 research outputs found
Computer-Assisted Program Reasoning Based on a Relational Semantics of Programs
We present an approach to program reasoning which inserts between a program
and its verification conditions an additional layer, the denotation of the
program expressed in a declarative form. The program is first translated into
its denotation from which subsequently the verification conditions are
generated. However, even before (and independently of) any verification
attempt, one may investigate the denotation itself to get insight into the
"semantic essence" of the program, in particular to see whether the denotation
indeed gives reason to believe that the program has the expected behavior.
Errors in the program and in the meta-information may thus be detected and
fixed prior to actually performing the formal verification. More concretely,
following the relational approach to program semantics, we model the effect of
a program as a binary relation on program states. A formal calculus is devised
to derive from a program a logic formula that describes this relation and is
subject for inspection and manipulation. We have implemented this idea in a
comprehensive form in the RISC ProgramExplorer, a new program reasoning
environment for educational purposes which encompasses the previously developed
RISC ProofNavigator as an interactive proving assistant.Comment: In Proceedings THedu'11, arXiv:1202.453
Sciduction: Combining Induction, Deduction, and Structure for Verification and Synthesis
Even with impressive advances in automated formal methods, certain problems
in system verification and synthesis remain challenging. Examples include the
verification of quantitative properties of software involving constraints on
timing and energy consumption, and the automatic synthesis of systems from
specifications. The major challenges include environment modeling,
incompleteness in specifications, and the complexity of underlying decision
problems.
This position paper proposes sciduction, an approach to tackle these
challenges by integrating inductive inference, deductive reasoning, and
structure hypotheses. Deductive reasoning, which leads from general rules or
concepts to conclusions about specific problem instances, includes techniques
such as logical inference and constraint solving. Inductive inference, which
generalizes from specific instances to yield a concept, includes algorithmic
learning from examples. Structure hypotheses are used to define the class of
artifacts, such as invariants or program fragments, generated during
verification or synthesis. Sciduction constrains inductive and deductive
reasoning using structure hypotheses, and actively combines inductive and
deductive reasoning: for instance, deductive techniques generate examples for
learning, and inductive reasoning is used to guide the deductive engines.
We illustrate this approach with three applications: (i) timing analysis of
software; (ii) synthesis of loop-free programs, and (iii) controller synthesis
for hybrid systems. Some future applications are also discussed
On the organisation of program verification competitions
In this paper, we discuss the challenges that have to be addressed when organising program verification competitions. Our focus is on competitions for verification systems where the participants both formalise an informally stated requirement and (typically) provide some guidance for the tool to show it. The paper draws its insights from our experiences with organising a program verification competition at FoVeOOS 2011. We discuss in particular the following aspects: challenge selection, on-site versus online organisation, team composition and judging. We conclude with a list of recommendations for future competition organisers
Overview on agent-based social modelling and the use of formal languages
Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft
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